In order to enhance the reconstruction ability of regularized super-resolution technique for noisy image, an adaptive threshold denoising method was proposed based on the extended research of General Total Variation (GTV) regularized super-resolution reconstruction. Firstly, the iterative reconstruction was completed according to GTV regularized super-resolution reconstruction. Then, the deduced adaptive threshold matrix was used to divide GTV cost matrix of each iteration procedure by the threshold. The corresponding pixel points whose costs were less than the threshold continued to be iterated while the points whose costs were greater than the threshold were cut down for re-interpolating and canceled from the iteration of this turn. Finally, the reconstruction result was output when the program met the convergence requirement. The experimental results show that, compared with the single GTV regularized reconstruction method and adaptive parameter method, the proposed adaptive threshold denoising method accelerates the convergence rate and improves the quality of reconstruction image, which makes the regularized super-resolution reconstruction technology perform better for noisy image.

As a new service for data storage and management, cloud storage has the virtue of portability and simplicity in use. However, it also prompts a significant problem of ensuring the integrity and recovery of data. A data recovery system for cloud storage based on fountain code was designed to resolve the problem. In this system, the user encoded his data by fountain code to make the tampered data recoverable, and tested the data's integrity with Hash functions so that the complexity in data verification and recovery was reduced. Through this system, the user can verify whether his data have been tampered or not by sending a challenge to the servers. Furthermore, once some data have been found tampered, the user can require and supervise the servers to locate and repair them timely. The experimental results show that the data integrity detection precision reaches 99% when the data's manipulation rate is 1%-5%.

To solve the issue that, in wireless resource competition, the environment information which gamers get in power game is asymmetric, a power game mechanism based on hidden Markov prediction was proposed. By establishing a Hidden Markov Prediction Model (HMPM), the proposed mechanism estimated whether competitors would take part in the game to improve the information accuracy of the game. Then, the predicted information was used to calculate the best transmission power via the cost function. The simulation results show that, compared with MAP (Maximum A Posteriori) method and NP (No Predicting) method, the power game model based on hidden Markov prediction can not only meet the target capacity, but also improve the power efficiency of the unauthorized users.

To solve the problem of transmission in wireless networks, a transmission and scheduling scheme based on W-learning algorithm in wireless networks was proposed in this paper. Building the system model based on Markov Decision Progress (MDP), with the help of W-learning algorithm, the goal of using this scheme was to transmit intelligently, namely, the package loss under the premise of energy saving by choosing which one to transmit and the transmit mode legitimately was reduced. The curse of dimensionality was overcome by state aggregate method, and the number of actions was reduced by action set reduction scheme. The storage space compression ratio of successive approximation was 41%; the storage space compression ratio of W-learning algorithm was 43%. Finally, the simulation results were given to evaluate the performances of the scheme, which showed that the proposed scheme can transport data as much as possible on the basis of energy saving, the state aggregation method and the action set reduction scheme can simplify the calculation with little influence on the performance of algorithms.

To compress image sequence, a coder structure, which combines the standard 3-D wavelet transform with motion compensation, was introduced. For image sequence with slow motion, according to the characteristic of the wavelet transformed data, a new coding algorithm, which integrated the improved EZW (Embedded Zerotree Wavelets Encoding) and pixel coding, was presented. Computer simulation shows that through the method not only the computational complexity is reduced but also even when the compress ratio gets 400:1, the recovered image quality still can gain more than 29dB.

An object-oriented approach to the design and implementation of micro-communication system based on UML(Unified Modeling Language) was proposed . It can satisfy various requirements in micro-communication system, and it is visualized, vivid and legible for developers to understand and implement. Based on abstract, analysis and design, a simple model for connectionless service was also represented.